Evolutionary spectrum estimation for uniformly modulated processes with improved frequency resolution

Author(s):  
Azadeh Moghtaderi ◽  
Glen Takahara ◽  
David J. Thomson
2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Danhui Dan ◽  
Jiongxin Gong ◽  
Yiming Zhao

We propose a 2D representation in the frequency-decay factor plane of an arbitrary real-world vibration signal. The signal is expressed as the sum of a decayed-attenuation sine term modulated by an amplitude function and a noise residue. We extend the combined approach of Capon estimation and amplitude and phase estimation (CAPES) to damped real vibration signals (DR-CAPES). In the proposed DR-CAPES method, the high-resolution amplitude and phase are estimated simultaneously for both angular frequency and decay factor grids. The performance of the proposed approach is tested numerically with noisy vibration data. Results show that the DR-CAPES method has an excellent frequency resolution, which helps to overcome difficulties in spectrum estimation when vibration modes are very close, and a small bias, which makes it suitable for obtaining accurate amplitude spectrums. The results also indicate that the proposed method can accurately estimate the amplitude spectrum with the use of averaging and denoising processes.


2013 ◽  
Vol 734-737 ◽  
pp. 2622-2629
Author(s):  
Shen Liu ◽  
Fu Ping Wang ◽  
Xiu Cheng Liu

This paper focused on UM2000 signal spectrum estimation using MUSIC algorithm. Because of the limitation of data window length, traditional frequency discrimination methods fail to meet the requirement of high frequency resolution. In this paper, the influence of SNR on MUSIC spectrum estimation is analyzed and MDL (minimum description length) principle is used to determine the dimension of the signal. Simulation results based on several other modern spectral estimation methods are also presented and compared with that of MUSIC method, from which the superiority of MUSIC method is verified.


2010 ◽  
Vol 51 ◽  
Author(s):  
Gintarė Petreikytė ◽  
Kazys Kazlauskas

The subject of this paper is the comparative analysis of the eleven most important nonparametric, parametric and subspace power spectrum estimation methods. Theoretically and experimentally we analyse how the frequency resolution of the spectrum estimation methods depends on the signal length, signal-to-noise ratio (SNR) and the order parametric methods.


2021 ◽  
Author(s):  
Sudeshna Pal

A novel approach to nonparametric spectral density estimation has been proposed. The approach is based on a new evaluation criterion called autocorrelation mean square error (AMSE) for power spectral density (PSD) estimates of available finite length data. Minimization of this criterion not only provides the optimum segmentation for existing PSDE approaches , but also provides a new optimum windowing within the segments that can be combined additionally to the existing methods of nonparametric PSDE. Furthermore, the problem of frequency resolution in existing PSDE methods for noisy signals has been analyzed. In the existing approaches, the additive noise and the finiteness of data which are the causes of the original loss of the frequency resolution are not treated separately. The suggested new approach to spectrum estimation takes advantage of these two different causes of the problem and tackles the problem of resolution in two steps. First, the method optimally reduces noise interference with the signal via minimum noiseless description length (MNDL). The new power spectrum estimation MNDL-Periodogram of the denoised signal is then computed via conventional indirect periodogram to improve frequency resolution.


2021 ◽  
Author(s):  
Sudeshna Pal

A novel approach to nonparametric spectral density estimation has been proposed. The approach is based on a new evaluation criterion called autocorrelation mean square error (AMSE) for power spectral density (PSD) estimates of available finite length data. Minimization of this criterion not only provides the optimum segmentation for existing PSDE approaches , but also provides a new optimum windowing within the segments that can be combined additionally to the existing methods of nonparametric PSDE. Furthermore, the problem of frequency resolution in existing PSDE methods for noisy signals has been analyzed. In the existing approaches, the additive noise and the finiteness of data which are the causes of the original loss of the frequency resolution are not treated separately. The suggested new approach to spectrum estimation takes advantage of these two different causes of the problem and tackles the problem of resolution in two steps. First, the method optimally reduces noise interference with the signal via minimum noiseless description length (MNDL). The new power spectrum estimation MNDL-Periodogram of the denoised signal is then computed via conventional indirect periodogram to improve frequency resolution.


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